code: def detect(img_file, compiled_model, re_shape, class_label):
      output = compiled_model.infer_new_request({0: test_image})
      result_ie = list(output.values())  # [0]
  
      test_im_shape = np.array([[re_shape, re_shape]]).astype("float32")
      test_scale_factor = np.array([[1, 1]]).astype("float32")
  
      np_score_list = []
      np_boxes_list = []
  
      num_outs = int(len(result_ie) / 2)
      for out_idx in range(num_outs):
          np_score_list.append(result_ie[out_idx])
          np_boxes_list.append(result_ie[out_idx + num_outs])
  
      postprocess = PicoDetPostProcess(
          test_image.shape[2:], test_im_shape, test_scale_factor
      )
  
      np_boxes, np_boxes_num = postprocess(np_score_list, np_boxes_list)
  
      image = cv2.imread(img_file, 1)
      scale_x = image.shape[1] / test_image.shape[3]
      scale_y = image.shape[0] / test_image.shape[2]
      res_image = draw_box(
          image, np_boxes, class_label, scale_x, scale_y
      )
  
      cv2.imwrite("res.jpg", res_image)
      cv2.imshow("res", res_image)
      cv2.waitKey()
  
file_path: PaddleDetection/deploy/third_engine/demo_openvino/python/openvino_benchmark.py
imports: import cv2
  import numpy
  import time
  import argparse
  from scipy.special import softmax
  from openvino.runtime import Core
label_desc: Use image node_id 6 for calling a global function with example usage: detect(img_file, compiled_model, re_shape, class_label) without return types